论文标题
REED在Semeval-2020任务9：微调和单词范围的方法用于代码混合情感分析
Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis
论文作者
论文摘要
我们探讨了有关hinglish（混合印度英语）推文的情感分析任务，这是Semeval-2020竞赛任务9的参与者，即Sentimix任务。我们采用了两种主要方法：1）通过对预训练的BERT模型进行调整学习，以及2）在单词袋表示上训练前馈神经网络。在竞争的评估阶段，我们以最佳模型获得了71.3％的F-评分，在官方系统排名中的62个条目中，$ 4^{th} $。
We explore the task of sentiment analysis on Hinglish (code-mixed Hindi-English) tweets as participants of Task 9 of the SemEval-2020 competition, known as the SentiMix task. We had two main approaches: 1) applying transfer learning by fine-tuning pre-trained BERT models and 2) training feedforward neural networks on bag-of-words representations. During the evaluation phase of the competition, we obtained an F-score of 71.3% with our best model, which placed $4^{th}$ out of 62 entries in the official system rankings.
